Value Router proposal and merge-value scoring for Agent Orchestrator

value-router-merge-value.md

Value Router Proposal for Agent Orchestrator

Single Smartest Addition to the Plan Right Now

Add a closed-loop Value Router (autonomous work allocator) that scores backlog items by expected merge value per agent-hour, then dynamically spawns/pauses/resumes sessions based on live outcomes.

Why This Is the Highest-Leverage Gap

• You already have backlog auto-claim and decomposition, but it is mostly label/FIFO + fixed capacity (MAX_CONCURRENT_AGENTS = 5) in services.ts.
• You already collect rich telemetry (success/failure, reasons, durations) in observability, but it is not yet feeding scheduling decisions (observability.md).
• You already have reaction/lifecycle machinery; this would become the optimizer layer above it.

What Makes It Radically Accretive

• It turns AO from "automation runner" into a self-optimizing execution system.
• Every run improves future prioritization (which issue types, repos, agents, and decomposition patterns actually ship fastest with least rework).
• It compounds throughput and merge quality without increasing operator overhead.

If useful, the next step is a minimal V1 spec and an initial scorer + scheduler loop behind a config flag.

How to Compute Merge Value

Use a normalized expected-value score per candidate issue:

MergeValue = (P_merge_24h * BusinessImpact * StrategicFit * Confidence) / ExpectedAgentHours

Practical Breakdown

• P_merge_24h (0..1): model from your own history by repo/label/agent/type.
  • Signals: similar issue merged rate, CI pass-on-first-try rate, review churn, reopen rate.
• BusinessImpact (1..5): configurable weight from labels/metadata.
  • Example: customer-facing=5, reliability=4, tech-debt=2.
• StrategicFit (0.8..1.3): multiplier for current priorities.
  • Example: if this sprint targets onboarding, onboarding-labeled issues get +30%.
• Confidence (0.6..1.2): evidence quality factor.
  • Clear acceptance criteria/tests/decomposition quality increase it.
• ExpectedAgentHours: predicted completion time from past runs.
  • Include expected retries from CI/review loops.

Scheduling Policy

Schedule by descending MergeValue subject to constraints:

• max parallel sessions,
• fairness per project,
• dependency/blocked checks.

Start Simple

1. Hand-tuned weights + rule-based estimates.
2. Log predicted vs actual.
3. Replace each component with learned models once you have enough data.